Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease
In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of t...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-01-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2021.755321/full |
| _version_ | 1797956407752916992 |
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| author | Mohd Murshad Ahmed Safia Tazyeen Shafiul Haque Ahmad Alsulimani Rafat Ali Mohd Sajad Aftab Alam Shahnawaz Ali Hala Abubaker Bagabir Rania Abubaker Bagabir Romana Ishrat |
| author_facet | Mohd Murshad Ahmed Safia Tazyeen Shafiul Haque Ahmad Alsulimani Rafat Ali Mohd Sajad Aftab Alam Shahnawaz Ali Hala Abubaker Bagabir Rania Abubaker Bagabir Romana Ishrat |
| author_sort | Mohd Murshad Ahmed |
| collection | DOAJ |
| description | In fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network. |
| first_indexed | 2024-04-10T23:48:33Z |
| format | Article |
| id | doaj.art-d6439c9846af413e92321e122b70cdf5 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-10T23:48:33Z |
| publishDate | 2022-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-d6439c9846af413e92321e122b70cdf52023-01-10T22:05:52ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-01-01810.3389/fcvm.2021.755321755321Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney DiseaseMohd Murshad Ahmed0Safia Tazyeen1Shafiul Haque2Ahmad Alsulimani3Rafat Ali4Mohd Sajad5Aftab Alam6Shahnawaz Ali7Hala Abubaker Bagabir8Rania Abubaker Bagabir9Romana Ishrat10Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, IndiaCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, IndiaResearch and Scientific Unit, College of Nursing and Allied Health Science, Jazan University, Jazan, Saudi ArabiaDepartment of Medical Laboratory Technology, College of Applied Medical Sciences, Jazan University, Jazan, Saudi ArbiaDepartment of Bioscience, Jamia Millia Islamia, New Delhi, IndiaCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, IndiaCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, IndiaCentre for Stem Cell & Regenerative Medicine, KING' College London, Guy's Hospital, London, United KingdomDepartment of Medical Physiology, Faculty of Medicine, King Abdulaziz University, Rabigh, Saudi ArabiaDepartment of Hematology and Immunology, College of Medicine, Umm-Al-Qura University, Mecca, Saudi ArabiaCentre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, IndiaIn fact, the risk of dying from CVD is significant when compared to the risk of developing end-stage renal disease (ESRD). Moreover, patients with severe CKD are often excluded from randomized controlled trials, making evidence-based therapy of comorbidities like CVD complicated. Thus, the goal of this study was to use an integrated bioinformatics approach to not only uncover Differentially Expressed Genes (DEGs), their associated functions, and pathways but also give a glimpse of how these two conditions are related at the molecular level. We started with GEO2R/R program (version 3.6.3, 64 bit) to get DEGs by comparing gene expression microarray data from CVD and CKD. Thereafter, the online STRING version 11.1 program was used to look for any correlations between all these common and/or overlapping DEGs, and the results were visualized using Cytoscape (version 3.8.0). Further, we used MCODE, a cytoscape plugin, and identified a total of 15 modules/clusters of the primary network. Interestingly, 10 of these modules contained our genes of interest (key genes). Out of these 10 modules that consist of 19 key genes (11 downregulated and 8 up-regulated), Module 1 (RPL13, RPLP0, RPS24, and RPS2) and module 5 (MYC, COX7B, and SOCS3) had the highest number of these genes. Then we used ClueGO to add a layer of GO terms with pathways to get a functionally ordered network. Finally, to identify the most influential nodes, we employed a novel technique called Integrated Value of Influence (IVI) by combining the network's most critical topological attributes. This method suggests that the nodes with many connections (calculated by hubness score) and high spreading potential (the spreader nodes are intended to have the most impact on the information flow in the network) are the most influential or essential nodes in a network. Thus, based on IVI values, hubness score, and spreading score, top 20 nodes were extracted, in which RPS27A non-seed gene and RPS2, a seed gene, came out to be the important node in the network.https://www.frontiersin.org/articles/10.3389/fcvm.2021.755321/fullCVDCKDPPIN networkIVIhubness scorespreading score |
| spellingShingle | Mohd Murshad Ahmed Safia Tazyeen Shafiul Haque Ahmad Alsulimani Rafat Ali Mohd Sajad Aftab Alam Shahnawaz Ali Hala Abubaker Bagabir Rania Abubaker Bagabir Romana Ishrat Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease Frontiers in Cardiovascular Medicine CVD CKD PPIN network IVI hubness score spreading score |
| title | Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease |
| title_full | Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease |
| title_fullStr | Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease |
| title_full_unstemmed | Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease |
| title_short | Network-Based Approach and IVI Methodologies, a Combined Data Investigation Identified Probable Key Genes in Cardiovascular Disease and Chronic Kidney Disease |
| title_sort | network based approach and ivi methodologies a combined data investigation identified probable key genes in cardiovascular disease and chronic kidney disease |
| topic | CVD CKD PPIN network IVI hubness score spreading score |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2021.755321/full |
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